64 accepted papers
Collision detection between robotic hands and manipulated objects is crucial to model predictive control (MPC) for contact-rich dexterous manipulation. Based on the Gilbert-Johnson-Keerthi (GJK) algorithm and the expanding polytope algorithm (EPA), the GJK-EPA method has achieved success while requi…
Retinal Surgery Robotics is a rapidly emerging field that offers enhanced precision by overcoming human tremors. A key trend of these robotic designs is toward more compact and lightweight structures for improved positioning accuracy and precise force delivery. However, this compactness sacrifices t…
Diffusion policies are powerful visuomotor models for robotic manipulation, yet they often fail to generalize to manipulators or end-effectors unseen during training and struggle to accommodate new task requirements at inference time. Addressing this typically requires costly data recollection and p…
High-quality LiDAR point cloud (LPC) compression is essential for the storage and transmission of 3D data. The octree-structured entropy codec has emerged as the predominant method; however, previous methods do not fully utilize spatial contextual information, due to the loss of local features cause…
To enhance the interpretability of multimodal large language models' outputs, recent efforts explored Grounded Visual Reasoning (GVR), in which the model is trained to select relevant image regions before answering the question. However, the multi-round ``ground-then-answer'' and reasoning nature of…
Unmanned aerial vehicle (UAV) autopilot systems typically comprise navigation and flight-control modules, and their effective scheduling is critical to achieving high flight performance. However, most existing UAV platforms adopt a split architecture in which navigation and flight control are deploy…
Recent advancements in Large Visual Language Models (LVLMs) have gained significant attention due to their remarkable reasoning capabilities and proficiency in generalization. However, processing a large number of visual tokens and generating long-context outputs impose substantial computational ove…
Capsulorhexis is challenging in cataract surgery, since the size, centering, and circularity of the capsule are important. Those indicators are closely related to the subsequent step of phacoemulsification and the postoperative position of the intraocular lens. It takes 3-5 years for a resident to p
Unmanned Aerial Vehicles (Uavs) are limited by the onboard energy. Refinement of the navigation strategy directly affects both the flight velocity and the trajectory based on the adjustment of key parameters in the Uavs pipeline, thus reducing energy consumption. However, existing techniques tend to
Applying pretraining-finetuning paradigm to event cameras presents significant challenges due to the scarcity of large-scale event datasets and the inherently sparse nature of event data, which increases the risk of overfitting during extensive pretraining.In this paper, we explore the transfer of p…
Gaze direction serves as a pivotal indicator for assessing the level of driver attention. While image-based gaze estimation has been extensively researched, there has been a recent shift towards capturing gaze direction from video sequences. This approach encounters notable challenges, including the…
Fully sparse 3D detectors have recently gained significant attention due to their efficiency in long-range detection. However, sparse 3D detectors extract features only from non-empty voxels, which impairs long-range interactions and causes the center feature missing. The former weakens the feature…
Multi-sensor fusion is crucial for improving the performance and robustness of end-to-end autonomous driving systems. Existing methods predominantly adopt either attention-based flatten fusion or bird’s eye view fusion through geometric transformations. However, these approaches often suffer from li…
In ophthalmic surgery, surgeons or robots manipulate a light probe and an instrument around two separated trocars following sclerotomy to achieve orbital control for eyeball pose adjustment and subsequent surgical tasks referring to microscope frames. However, current methods face significant challe
Machine learning models often perform well on tabular data by optimizing average prediction accuracy. However, they may underperform on specific subsets due to inherent biases and spurious correlations in the training data, such as associations with non-causal features like demographic information.…
Typical robot controllers assume firm ground, limiting their effectiveness in controlling robots on dynamic platforms such as trucks or ships. To address this limitation, we propose a reinforcement learning framework for robot locomotion on dynamic rigid platforms and a simulation in which 6-DoF dyn
Ophthalmic surgical robots offer superior stability and precision by reducing the natural hand tremors of human surgeons, enabling delicate operations in confined surgical spaces. Despite the advancements in developing vision- and force-based control methods for surgical robots, preoperative navigat
Point cloud compression is an essential technology for efficient storage and transmission of 3D data. Previous methods usually use hierarchical tree data structures for encoding the spatial sparseness of point clouds. However, the node context within the tree is not fully discovered since the featur…
With the development and application of swarm drones, some researchers have tried to replicating the migration patterns of geese in drones swarm formation to extend their lifetime. However, the problem of performing appropriate position reconfiguration based on the battery energy still remains an un
In robot-assisted retinal surgery, a Remote Center of Motion (Rcm) allows the surgical instrument to rotate around a distal fixed point without any lateral translations. The Rcm point should be perfectly aligned inside the trocar. Otherwise, unexpected tool translations at the expected remote center…
Event Stream Super-Resolution (ESR) aims to address the challenge of insufficient spatial resolution in event streams which holds great significance for the application of event cameras in complex scenarios. Previous works for ESR often process positive and negative events in a mixed paradigm. This…
Recently, significant progress has been made in the research of 3D object detection. However, most prior studies have focused on the utilization of center-based or anchor-based label assignment schemes. Alternative label assignment strategies remain unexplored in 3D object detection. We find that th…
Current Event Stream Super-Resolution (ESR) methods overlook the redundant and complementary information present in positive and negative events within the event stream, employing a direct mixing approach for super-resolution, which may lead to detail loss and inefficiency. To address these issues,…
Sea surface temperature (SST) plays an important role in our Earth’s atmosphere, wielding significant influence over both local and global climates and profoundly impacting ecosystems. However, this task presents unique challenges due to the inherent complexity and uncertainty within ocean systems.…
Robotic ophthalmic surgery is an emerging technology to facilitate high-precision interventions such as subretinal injection and removing swinging tissues in retinal detachment using microscopy and iOCT. However, locating the instrument tip outside iOCT's range-limited ROI is challenging, especially
LiDAR-based 3D object detection has made impressive progress recently, yet most existing models are black-box, lacking interpretability. Previous explanation approaches primarily focus on analyzing image-based models and are not readily applicable to LiDAR-based 3D detectors. In this paper, we propo…
Intuitive enhancement of surgical precision in robotic retinal surgery highly depends on the stable acquisition of intraocular imaging data. Such acquisition requires segmenting intraocular components, especially instrument-tip positions, to achieve state estimation and subsequent navigation and mot
Simultaneous localization and mapping (SLAM) is essential for autonomous driving. Most LiDAR-inertial SLAM algorithms assume a static environment, leading to unreliable localization in dynamic environments. Moreover, the accurate tracking of moving objects is of great significance for the control an
Uncertainty in LiDAR measurements, stemming from factors such as range sensing, is crucial for LIO (LiDAR-Inertial Odometry) systems as it affects the accurate weighting in the loss function. While recent LIO systems address uncertainty related to range sensing, the impact of incident angle on uncer
Local geometric information, i.e., normal and distribution of points, is crucial for LiDAR-based simultaneous localization and mapping (SLAM) because it provides constraints for data association, which further determines the direction of optimization and ultimately affects the accuracy of localizati
The growing interest in language-conditioned robot manipulation aims to develop robots capable of understanding and executing complex tasks, with the objective of enabling robots to interpret language commands and manipulate objects accordingly. While language-conditioned approaches demonstrate impr
Accurate and dense mapping in large-scale environments is essential for various robot applications. Recently, implicit neural signed distance fields (SDFs) have shown promising advances in this task. However, most existing approaches employ projective distances from range data as SDF supervision, in
Balancing oneself using the spine is a physiological alignment of the body posture in the most efficient manner by the muscular forces for mammals. For this reason, we can see many disabled quadruped animals can still stand or walk even with three limbs. This paper investigates the optimization of d…
Cooperative Localization (CL) plays a crucial role in achieving precise localization without relying on localization sensors. However, the performance of CL can be significantly affected by failed communication and biased measurements. This letter presents a robust decentralized CL method that addre
In ophthalmic surgeries, the light probe is responsible for providing safe intraocular illumination and ensuring the visibility of the instrument and its shadow as the only available reference for qualitative depth estimation and landing point prediction in fundus microscopic images. To achieve sust…
In ophthalmic surgeries, such as vitreoretinal operations, surgeons rely on imaging systems, primarily microscopes, for real-time instrument monitoring and motion planning. However, novice surgeons struggle to extract 3D instrument positions from 2D microscope frames, necessitating extensive trial-a…
Fine-tuning is essential to adapting pre-trained large language models to downstream applications. With the increasing popularity of LLM-enabled applications, fine-tuning has been performed intensively worldwide, incurring a tremendous amount of computing costs that correspond to big carbon footprin…
Lane keeping, as a fundamental functionality of autonomous navigation, remains a challenging task for autonomous robots and vehicles. Recently, spiking neural networks (SNNs) have gained attention and research interest due to their biological plausibility and application potential on neuromorphic pr…
Meta-reinforcement learning (meta-RL) is a promising approach that enables the agent to learn new tasks quickly. However, most meta-RL algorithms show poor generalization in multi-task scenarios due to the insufficient task information provided only by rewards. Language-conditioned meta-RL improves…
Although deep reinforcement learning has recently been very successful at learning complex behaviors, it requires a tremendous amount of data to learn a task. One of the fundamental reasons causing this limitation lies in the nature of the trial-and-error learning paradigm of reinforcement learning,…
As research into continuum robots flourishes, there are more and more types of continuum robots, which require researchers to tirelessly design robot-specific motion control algorithms. Besides, the convergence time of control systems for continuum robots has received very little attention. In this…
Generalized Few-shot Semantic Segmentation (GFSS) extends Few-shot Semantic Segmentation (FSS) to simultaneously segment unseen classes and seen classes during evaluation. Previous works leverage additional branch or prototypical aggregation to eliminate the constrained setting of FSS. However, repr…
Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart. Besides dealing with this long-standing task in the spatial domain, a few approaches seek solutions in the frequency domain in consideration of the large discrepancy between spectra of sharp/degraded image p…
The aim of this research is to investigate the relationship between spinal flexion and quadruped locomotion in a rat robot equipped with a compliant spine, controlled by a central pattern generator (CPG). The study reveals that spinal flexion can enhance limb stride length, but it may also cause sig…
Simultaneous localization and mapping (SLAM) is one of the essential techniques and functionalities used by robots to perform autonomous navigation tasks. Inspired by the rodent hippocampus, this paper presents a biologically inspired SLAM system based on a LiDAR sensor using a hippocampal model to…
Data in the real world tends to exhibit a long-tailed label distribution, which poses great challenges for the training of neural networks in visual recognition. Existing methods tackle this problem mainly from the perspective of data quantity, i.e., the number of samples in each class. To be specif…
Retinal surgery is a complex medical procedure that requires exceptional expertise and dexterity. For this purpose, several robotic platforms are currently under development to enable or improve the outcome of microsurgical tasks. Since the control of such robots is often designed for navigation ins…
Point clouds from 3D light detection and ranging (LiDAR) are widely used. Noise caused by falling snow reduces the availability of point clouds. Due to the sparseness of LiDAR point clouds and the fact that the snow point clouds are easily affected by multi factors such as wind or snowfall condition…
In nature, the movement of quadrupeds is completed under the combined action of the spine and the legs. Inspired by this, this paper explores the effect of a lateral flexing spine on the locomotion of a rat robot. Benefiting from the regular lateral flexion of a soft actuated spine, the rat robot ex…
Semi-supervised few-shot learning is developed to train a classifier that can adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data. Most semi-supervised few-shot learning methods select pseudo-labeled data of unlabeled set by task-specific confidence estimation. This w…
Recent approaches for 3D object detection have made tremendous progresses due to the development of deep learning. However, previous researches are mostly based on individual frames, leading to limited exploitation of information between frames. In this paper, we attempt to leverage the temporal inf…
Clear Corneal Incision, a challenging step in cataract surgery, and important to the overall quality of the surgery. New surgeons usually spend one full year trying to perfect their incision, but even after such rigorous training deficient incisions can still occur. This paper proposes an autonomous…
This paper proposes an offline and runtime combined framework for the autonomous motion of snake robots. With the dynamic feedback of its state during runtime, the robot utilizes the linear regression to update its control parameters for better performance and thus adaptively reacts to the environme…
The snake-like robot without wheels is a bio-inspired robot whose high degree of freedom results in a challenge in autonomous locomotion control. The use of a Spiking Neural Network (SNN) which is a biologically plausible artificial neural network can help to achieve the autonomous locomotion behavi…
This paper introduces an end-to-end learning approach based on Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) for a multi-layered spiking neural network (SNN). As a case study, a snake-like robot is used as an agent to perform target tracking tasks on the basis of our proposed approach.…
Detection of the navigable regions for the unmanned surface vehicles (USVs) sailing on the narrow rivers is very important. Existing detection methods mostly depend on the cameras, which is sensitive to environments and cannot provide reliable navigable regions for sailing. In this paper, we propose…
Pedestrian detection has attracted enormous research attention in the field of Intelligent Transportation System (ITS) due to that pedestrians are the most vulnerable traffic participants. So far, almost all pedestrian detection solutions are based on the conventional frame-based camera. However, th…
Subretinal injection is known to be a complicated task for ophthalmologists to perform, the main sources of difficulties are the fine anatomy of the retina, insufficient visual feedback, and high surgical precision. Image guided robot-assisted surgery is one of the promising solutions that bring sig…
Learning-based methods have demonstrated clear advantages in controlling robot tasks, such as the information fusion abilities, strong robustness, and high accuracy. Meanwhile, the on-board systems of robots have limited computation and energy resources, which are contradictory with state-of-the-art…
Subretinal injection is a delicate and complex microsurgery, which requires surgeons to inject the therapeutic substance in a pre-operatively defined and intra-operatively updated subretinal target area. Due to the lack of subretinal visual feedback, it is hard to sense the insertion depth during th…
Ophthalmic microsurgery is known to be a challenging operation, which requires very precise and dexterous manipulation. Image guided robot-assisted surgery is a promising solution that brings significant improvements in outcomes and reduces the physical limitations of human surgeons. However, this t
In this paper, a lightweight central pattern generator(CPG) model is designed for a snake-like robot, to achieve smooth transition of body shape and locomotion speed. First, based on the convergence behavior of the gradient system, a lightweight CPG model with fast computing time is designed and com…
Visual SLAM in low illumination scenes remains a considerably challenging task since the available amount of appearance information frequently stays insufficient. To tackle with this problem, we propose a novel SLAM framework by using both appearance information and thermal information, which posses…
In this paper, a biologically inspired 3D slithering gait for a snake-like robot is designed and implemented for the purpose of target tracking. First, by balancing the forward speed and the stability of the robot, a straight slithering gait is modelled, under which the robot can march straight, fas…